MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) WITH ADAPTIVE WEIGHTING FUNCTION IN POVERTY MODELING IN NTT PROVINCE
Poverty modeling is a crucial economic and social development issue in various regions, including in East Nusa Tenggara (NTT) Province. This research proposes using the Mixed Geographically Weighted Regression (MGWR) model with an adaptive Bisquare weighting function to analyze variables influencing...
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Universitas Pattimura
2024-07-01
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12969 |
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| author | Petrus Kanisius Ola Atiek Iriany Suci Astutik |
| author_facet | Petrus Kanisius Ola Atiek Iriany Suci Astutik |
| author_sort | Petrus Kanisius Ola |
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| description | Poverty modeling is a crucial economic and social development issue in various regions, including in East Nusa Tenggara (NTT) Province. This research proposes using the Mixed Geographically Weighted Regression (MGWR) model with an adaptive Bisquare weighting function to analyze variables influencing poverty levels in NTT Province. The MGWR model is an extension of the Geographically Weighted Regression (GWR), which allows some variables in the model to have local effects while others have global effects. The adaptive weighting function in the MGWR model enhances the analysis by providing different weights at each location according to its local characteristics, thus making the results more accurate and representative for each area. The data includes economic, social, and infrastructure variables from 22 districts/cities in NTT Province for 2023. The MGWR model with an adaptive weighting function is applied to model the relationship between these variables and poverty levels. The analysis integrates statistical software to manage and analyze spatial data. The study findings show that the MGWR model with an adaptive weighting function offers better estimates than the global regression and GWR models. The results revealed the smallest AIC value for the MGWR model at 104.1888, compared to the global regression model at 140.1427 and the GWR model at 117.6174. This model successfully identifies significant local and global variables and shows variations in influence at different locations in NTT Province. These findings provide valuable insights for policymakers and practitioners in designing and implementing more effective poverty alleviation strategies tailored to local conditions in NTT Province. |
| format | Article |
| id | doaj-art-2d5ca74c4c754a8a8ebbfb64089a85bb |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Universitas Pattimura |
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| spelling | doaj-art-2d5ca74c4c754a8a8ebbfb64089a85bb2025-08-20T03:49:07ZengUniversitas PattimuraBarekeng1978-72272615-30172024-07-011832035204610.30598/barekengvol18iss3pp2035-204612969MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) WITH ADAPTIVE WEIGHTING FUNCTION IN POVERTY MODELING IN NTT PROVINCEPetrus Kanisius Ola0Atiek Iriany1Suci Astutik2Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, IndonesiaPoverty modeling is a crucial economic and social development issue in various regions, including in East Nusa Tenggara (NTT) Province. This research proposes using the Mixed Geographically Weighted Regression (MGWR) model with an adaptive Bisquare weighting function to analyze variables influencing poverty levels in NTT Province. The MGWR model is an extension of the Geographically Weighted Regression (GWR), which allows some variables in the model to have local effects while others have global effects. The adaptive weighting function in the MGWR model enhances the analysis by providing different weights at each location according to its local characteristics, thus making the results more accurate and representative for each area. The data includes economic, social, and infrastructure variables from 22 districts/cities in NTT Province for 2023. The MGWR model with an adaptive weighting function is applied to model the relationship between these variables and poverty levels. The analysis integrates statistical software to manage and analyze spatial data. The study findings show that the MGWR model with an adaptive weighting function offers better estimates than the global regression and GWR models. The results revealed the smallest AIC value for the MGWR model at 104.1888, compared to the global regression model at 140.1427 and the GWR model at 117.6174. This model successfully identifies significant local and global variables and shows variations in influence at different locations in NTT Province. These findings provide valuable insights for policymakers and practitioners in designing and implementing more effective poverty alleviation strategies tailored to local conditions in NTT Province.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12969povertygwrmgwrregression |
| spellingShingle | Petrus Kanisius Ola Atiek Iriany Suci Astutik MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) WITH ADAPTIVE WEIGHTING FUNCTION IN POVERTY MODELING IN NTT PROVINCE Barekeng poverty gwr mgwr regression |
| title | MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) WITH ADAPTIVE WEIGHTING FUNCTION IN POVERTY MODELING IN NTT PROVINCE |
| title_full | MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) WITH ADAPTIVE WEIGHTING FUNCTION IN POVERTY MODELING IN NTT PROVINCE |
| title_fullStr | MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) WITH ADAPTIVE WEIGHTING FUNCTION IN POVERTY MODELING IN NTT PROVINCE |
| title_full_unstemmed | MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) WITH ADAPTIVE WEIGHTING FUNCTION IN POVERTY MODELING IN NTT PROVINCE |
| title_short | MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) WITH ADAPTIVE WEIGHTING FUNCTION IN POVERTY MODELING IN NTT PROVINCE |
| title_sort | mixed geographically weighted regression mgwr with adaptive weighting function in poverty modeling in ntt province |
| topic | poverty gwr mgwr regression |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12969 |
| work_keys_str_mv | AT petruskanisiusola mixedgeographicallyweightedregressionmgwrwithadaptiveweightingfunctioninpovertymodelinginnttprovince AT atiekiriany mixedgeographicallyweightedregressionmgwrwithadaptiveweightingfunctioninpovertymodelinginnttprovince AT suciastutik mixedgeographicallyweightedregressionmgwrwithadaptiveweightingfunctioninpovertymodelinginnttprovince |